






'''simple docstring'''

import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.generation import (
					    FlaxForcedBOSTokenLogitsProcessor,
					    FlaxForcedEOSTokenLogitsProcessor,
					    FlaxLogitsProcessorList,
					    FlaxMinLengthLogitsProcessor,
					    FlaxTemperatureLogitsWarper,
					    FlaxTopKLogitsWarper,
					    FlaxTopPLogitsWarper,
					)






@require_flax
class     a_						(      unittest.TestCase    ):

		def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



								"""simple docstring"""






								UpperCamelCase      = jnp.ones((batch_size, length)				) / length
								return scores

		def   A__						(				self				)  ->				Optional[Any]:



								"""simple docstring"""






								UpperCamelCase      = None
								UpperCamelCase      = 20

								UpperCamelCase      = self._get_uniform_logits(batch_size=2      ,		length=_SCREAMING_SNAKE_CASE				)

								# tweak scores to not be uniform anymore
								UpperCamelCase      = scores.at[1, 5].set((1 / length) + 0.1				)  # peak, 1st batch
								UpperCamelCase      = scores.at[1, 10].set((1 / length) - 0.4				)  # valley, 1st batch

								# compute softmax
								UpperCamelCase      = jax.nn.softmax(_SCREAMING_SNAKE_CASE      ,		axis=-1				)

								UpperCamelCase      = FlaxTemperatureLogitsWarper(temperature=0.5				)
								UpperCamelCase      = FlaxTemperatureLogitsWarper(temperature=1.3				)

								UpperCamelCase      = jax.nn.softmax(temp_dist_warper_sharper(_SCREAMING_SNAKE_CASE      ,		scores.copy()      ,		cur_len=_SCREAMING_SNAKE_CASE				)      ,		axis=-1				)
								UpperCamelCase      = jax.nn.softmax(temp_dist_warper_smoother(_SCREAMING_SNAKE_CASE      ,		scores.copy()      ,		cur_len=_SCREAMING_SNAKE_CASE				)      ,		axis=-1				)

								# uniform distribution stays uniform
								self.assertTrue(jnp.allclose(probs[0, :]      ,		warped_prob_sharp[0, :]      ,		atol=1e-3				)				)
								self.assertTrue(jnp.allclose(probs[0, :]      ,		warped_prob_smooth[0, :]      ,		atol=1e-3				)				)

								# sharp peaks get higher, valleys get lower
								self.assertLess(probs[1, :].max()      ,		warped_prob_sharp[1, :].max()				)
								self.assertGreater(probs[1, :].min()      ,		warped_prob_sharp[1, :].min()				)

								# smooth peaks get lower, valleys get higher
								self.assertGreater(probs[1, :].max()      ,		warped_prob_smooth[1, :].max()				)
								self.assertLess(probs[1, :].min()      ,		warped_prob_smooth[1, :].min()				)

		def   A__						(				self				)  ->				int:



								"""simple docstring"""






								UpperCamelCase      = None
								UpperCamelCase      = 10
								UpperCamelCase      = 2

								# create ramp distribution
								UpperCamelCase      = np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE				)[None, :]      ,		(batch_size, vocab_size)				).copy()
								UpperCamelCase      = ramp_logits[1:, : vocab_size // 2] + vocab_size

								UpperCamelCase      = FlaxTopKLogitsWarper(3				)

								UpperCamelCase      = top_k_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)

								# check that correct tokens are filtered
								self.assertListEqual(jnp.isinf(scores[0]				).tolist()      ,		7 * [True] + 3 * [False]				)
								self.assertListEqual(jnp.isinf(scores[1]				).tolist()      ,		2 * [True] + 3 * [False] + 5 * [True]				)

								# check special case
								UpperCamelCase      = 5
								UpperCamelCase      = FlaxTopKLogitsWarper(top_k=1      ,		filter_value=0.0      ,		min_tokens_to_keep=3				)

								UpperCamelCase      = np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE				)[None, :]      ,		(batch_size, length)				).copy()
								UpperCamelCase      = top_k_warp_safety_check(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)

								# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
								self.assertListEqual((scores == 0.0).sum(axis=-1				).tolist()      ,		[2, 2]				)

		def   A__						(				self				)  ->				List[Any]:



								"""simple docstring"""






								UpperCamelCase      = None
								UpperCamelCase      = 10
								UpperCamelCase      = 2

								# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
								UpperCamelCase      = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]				)				)

								UpperCamelCase      = FlaxTopPLogitsWarper(0.8				)
								UpperCamelCase      = np.exp(top_p_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)				)

								# dist should be filtered to keep min num values so that sum is >= top_p
								# exp (-inf) => 0
								UpperCamelCase      = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]				)
								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

								# check edge cases with negative and extreme logits
								UpperCamelCase      = np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE				)[None, :]      ,		(batch_size, vocab_size)				).copy() - (
								    vocab_size // 2
								)

								# make ramp_logits more extreme
								UpperCamelCase      = ramp_logits[1] * 100.0

								# make sure at least 2 tokens are kept
								UpperCamelCase      = FlaxTopPLogitsWarper(0.9      ,		min_tokens_to_keep=2      ,		filter_value=0.0				)
								UpperCamelCase      = top_p_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)

								# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
								self.assertListEqual((filtered_dist != 0.0).sum(axis=-1				).tolist()      ,		[3, 2]				)

		def   A__						(				self				)  ->				Optional[Any]:



								"""simple docstring"""






								UpperCamelCase      = 20
								UpperCamelCase      = 4
								UpperCamelCase      = 0

								UpperCamelCase      = FlaxMinLengthLogitsProcessor(min_length=10      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)

								# check that min length is applied at length 5
								UpperCamelCase      = ids_tensor((batch_size, 20)      ,		vocab_size=20				)
								UpperCamelCase      = 5
								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = min_dist_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()      ,		4 * [-float("""inf"""				)]				)

								# check that min length is not applied anymore at length 15
								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = 15
								UpperCamelCase      = min_dist_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE				).any()				)

		def   A__						(				self				)  ->				Tuple:



								"""simple docstring"""






								UpperCamelCase      = 20
								UpperCamelCase      = 4
								UpperCamelCase      = 0

								UpperCamelCase      = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE				)

								# check that all scores are -inf except the bos_token_id score
								UpperCamelCase      = ids_tensor((batch_size, 1)      ,		vocab_size=20				)
								UpperCamelCase      = 1
								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]				).all()				)
								self.assertListEqual(scores[:, bos_token_id].tolist()      ,		4 * [0]				)  # score for bos_token_id shold be zero

								# check that bos_token_id is not forced if current length is greater than 1
								UpperCamelCase      = 3
								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE				).any()				)

		def   A__						(				self				)  ->				List[Any]:



								"""simple docstring"""






								UpperCamelCase      = 20
								UpperCamelCase      = 4
								UpperCamelCase      = 0
								UpperCamelCase      = 5

								UpperCamelCase      = FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)

								# check that all scores are -inf except the eos_token_id when max_length is reached
								UpperCamelCase      = ids_tensor((batch_size, 4)      ,		vocab_size=20				)
								UpperCamelCase      = 4
								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]				).all()				)
								self.assertListEqual(scores[:, eos_token_id].tolist()      ,		4 * [0]				)  # score for eos_token_id should be zero

								# check that eos_token_id is not forced if max_length is not reached
								UpperCamelCase      = 3
								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE				).any()				)

		def   A__						(				self				)  ->				Optional[Any]:



								"""simple docstring"""






								UpperCamelCase      = 4
								UpperCamelCase      = 10
								UpperCamelCase      = 15
								UpperCamelCase      = 2
								UpperCamelCase      = 1
								UpperCamelCase      = 15

								# dummy input_ids and scores
								UpperCamelCase      = ids_tensor((batch_size, sequence_length)      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = input_ids.copy()

								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = scores.copy()

								# instantiate all dist processors
								UpperCamelCase      = FlaxTemperatureLogitsWarper(temperature=0.5				)
								UpperCamelCase      = FlaxTopKLogitsWarper(3				)
								UpperCamelCase      = FlaxTopPLogitsWarper(0.8				)

								# instantiate all logits processors
								UpperCamelCase      = FlaxMinLengthLogitsProcessor(min_length=10      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)

								UpperCamelCase      = 10

								# no processor list
								UpperCamelCase      = temp_dist_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = top_k_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = top_p_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = min_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = bos_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = eos_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)

								# with processor list
								UpperCamelCase      = FlaxLogitsProcessorList(
								    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]				)
								UpperCamelCase      = processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)

								# scores should be equal
								self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()      ,		input_ids_comp.tolist()				)


		def   A__						(				self				)  ->				Dict:



								"""simple docstring"""






								UpperCamelCase      = 4
								UpperCamelCase      = 10
								UpperCamelCase      = 15
								UpperCamelCase      = 2
								UpperCamelCase      = 1
								UpperCamelCase      = 15

								# dummy input_ids and scores
								UpperCamelCase      = ids_tensor((batch_size, sequence_length)      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = input_ids.copy()

								UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = scores.copy()

								# instantiate all dist processors
								UpperCamelCase      = FlaxTemperatureLogitsWarper(temperature=0.5				)
								UpperCamelCase      = FlaxTopKLogitsWarper(3				)
								UpperCamelCase      = FlaxTopPLogitsWarper(0.8				)

								# instantiate all logits processors
								UpperCamelCase      = FlaxMinLengthLogitsProcessor(min_length=10      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)

								UpperCamelCase      = 10

								# no processor list
								def run_no_processor_list(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
														UpperCamelCase      = temp_dist_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
														UpperCamelCase      = top_k_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
														UpperCamelCase      = top_p_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
														UpperCamelCase      = min_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
														UpperCamelCase      = bos_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
														UpperCamelCase      = eos_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
														return scores

								# with processor list
								def run_processor_list(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
														UpperCamelCase      = FlaxLogitsProcessorList(
														    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]				)
														UpperCamelCase      = processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
														return scores

								UpperCamelCase      = jax.jit(_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = jax.jit(_SCREAMING_SNAKE_CASE				)

								UpperCamelCase      = jitted_run_no_processor_list(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
								UpperCamelCase      = jitted_run_processor_list(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

								# scores should be equal
								self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()      ,		input_ids_comp.tolist()				)








'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger('transformers.models.speecht5')







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      hf_model.apply_weight_norm()

      UpperCamelCase      = checkpoint["""input_conv.weight_g"""]
      UpperCamelCase      = checkpoint["""input_conv.weight_v"""]
      UpperCamelCase      = checkpoint["""input_conv.bias"""]

      for i in range(len(config.upsample_rates   )   ):
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.weight_g"]
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.weight_v"]
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.bias"]

      for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
            for j in range(len(config.resblock_dilation_sizes   )   ):
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

      UpperCamelCase      = checkpoint["""output_conv.1.weight_g"""]
      UpperCamelCase      = checkpoint["""output_conv.1.weight_v"""]
      UpperCamelCase      = checkpoint["""output_conv.1.bias"""]

      hf_model.remove_weight_norm()







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			List[Any]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaHifiGanConfig()

      UpperCamelCase      = SpeechTaHifiGan(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      load_weights(orig_checkpoint["""model"""]["""generator"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

      UpperCamelCase      = np.load(__UpperCamelCase   )
      UpperCamelCase      = stats[0].reshape(-1   )
      UpperCamelCase      = stats[1].reshape(-1   )
      UpperCamelCase      = torch.from_numpy(__UpperCamelCase   ).float()
      UpperCamelCase      = torch.from_numpy(__UpperCamelCase   ).float()

      model.save_pretrained(__UpperCamelCase   )

      if repo_id:
            print("""Pushing to the hub..."""   )
            model.push_to_hub(__UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
     parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_hifigan_checkpoint(
         args.checkpoint_path,
         args.stats_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )
